Display defect detection apparatus and detection method, display defect detection system, and electronic device

ABSTRACT

A display defect detection method, includes: collecting at least one display image of at least one display to be detected; extracting a plurality of band-limited intrinsic mode function components from a display image in the at least one display image by using a complex variational mode decomposition method; extracting and fusing the plurality of band-limited intrinsic mode function components of the display image by using a convolutional neural network, so as to obtain an average brightness value and a brightness uniformity of the display image; and determining whether a display to be detected in the at least one display to be detected corresponding to the display image is qualified according to a preset classification rule and the average brightness value and the brightness uniformity of the display image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national phase entry under 35 USC 371 ofInternational Patent Application No. PCT/CN 2021/130068 filed on Nov.11, 2021, which claims priority to Chinese Patent Application No.202110461828.7, filed on Apr. 27, 2021, which are incorporated herein byreference in their entirety.

TECHNICAL FIELD

The present disclosure relates to the field of display technologies, andin particular, to a display defect detection apparatus and detectionmethod, and a display defect detection system.

BACKGROUND

A side light type backlight source is used in a backlight module of adisplay. The backlight module includes the backlight source and a lightguide plate, and the backlight source is disposed on a side of the lightguide plate. Light emitted from the backlight source is transmitted to adisplay panel of the display through the light guide plate.

However, the side light type backlight source causes the display todisplay a picture with a large brightness in a region close to thebacklight source and a small brightness in a region away from thebacklight source, so that the display picture shows a phenomenon ofuneven brightness (i.e., Hotspot phenomenon).

SUMMARY

In an aspect, a display defect detection method is provided. The displaydefect detection method includes as follows.

At least one display image of at least one display to be detected iscollected.

A plurality of band-limited intrinsic mode function components areextracted from a display image in the at least one display image byusing a complex variational mode decomposition method.

The plurality of band-limited intrinsic mode function components of thedisplay image are extracted and fused by using a convolutional neuralnetwork, so as to obtain an average brightness value and a brightnessuniformity of the display image.

It is determined whether a display to be detected in the at least onedisplay to be detected corresponding to the display image is qualifiedaccording to a preset classification rule and the average brightnessvalue and the brightness uniformity of the display image.

In some embodiments, the preset classification rule includes: if theaverage brightness value of the display image is greater than or equalto a preset brightness value, and the brightness uniformity of thedisplay image is greater than or equal to a preset brightnessuniformity, determining that the display to be detected corresponding tothe display image is qualified; and if not, determining that the displayto be detected corresponding to the display image is unqualified.

In some embodiments, before collecting the at least one display image ofthe at least one display to be detected, the display defect detectionmethod further includes as follows.

Display images of a plurality of sample displays are collected as sampleimages, and reference results of whether the sample displayscorresponding to respective sample images are qualified are obtained.

It is determined whether a sample display in the plurality of sampledisplays that each sample image corresponds to is qualified by using theconvolutional neural network, so as to obtain actual detection resultsof whether the plurality of sample displays are qualified. Theconvolutional neural network includes detection parameters.

An actual detection result of a sample display in the plurality ofsample displays is compared with a respective reference result todetermine whether the actual detection result is consistent with therespective reference result, and the detection parameters of theconvolutional neural network are adjusted according to a comparisonresult.

The detection parameters of the convolutional neural network areadjusted repeatedly, until the actual detection results of the pluralityof sample displays are stable.

In some embodiments, before determining whether the sample display thateach sample image corresponds to is qualified by using the convolutionalneural network, the display defect detection method further includes:extracting a plurality of band-limited intrinsic mode functioncomponents from the sample image by using the complex variational modedecomposition method.

In some embodiments, determining whether the sample display that eachsample image corresponds to is qualified by using the convolutionalneural network, includes as follows.

The plurality of band-limited intrinsic mode function components of thesample image are extracted and fused by using the convolutional neuralnetwork, so as to obtain an average brightness value and a brightnessuniformity of the sample image.

It is determined whether the sample display corresponding to the sampleimage is qualified according to the preset classification rule and theaverage brightness value and the brightness uniformity of the sampleimage.

In some embodiments, comparing the actual detection result of the sampledisplay with the respective reference result to determine whether theactual detection result is consistent with the respective referenceresult, and adjusting the detection parameters of the convolutionalneural network according to the comparison result, include: if theactual detection result of the sample display is inconsistent with therespective reference result, adjusting the detection parameters of theconvolutional neural network.

In some embodiments, the detection parameters include a kernel functionparameter and a penalty parameter.

In some embodiments, after collecting the at least one display image ofthe at least one display to be detected, the display defect detectionmethod further includes: preprocessing the display image. Thepreprocessing includes at least one of image cropping, graying andfiltering.

In some embodiments, after collecting the at least one display image ofthe at least one display to be detected, the display defect detectionmethod further includes: disposing a plurality of monitoring points onthe display image; and obtaining brightness information of the pluralityof monitoring points. The plurality of monitoring points are arranged inan array. A distance between two adjacent monitoring points in a firstdirection is substantially equal to a distance between two adjacentmonitoring points in a second direction. The first direction and thesecond direction intersect.

In some embodiments, extracting the plurality of band-limited intrinsicmode function components from the display image by using the complexvariational mode decomposition method, includes as follows.

Brightness information of each monitoring point on the display image isdecomposed into a plurality of modal components by using the complexvariational mode decomposition method.

At least one noise component in the plurality of modal components of themonitoring point is removed to extract band-limited intrinsic modefunction components in the plurality of modal components of themonitoring point.

In some embodiments, extracting and fusing the plurality of band-limitedintrinsic mode function components of the display image by using theconvolutional neural network to obtain the average brightness value andthe brightness uniformity of the display image, includes as follows.

Band-limited intrinsic mode function components of brightnessinformation of a monitoring point in the plurality of monitoring pointson the display image are extracted by using the convolutional neuralnetwork, so that a brightness corresponding to the band-limitedintrinsic mode function components of the monitoring point is used asthe average brightness value of the display image.

Alternatively, band-limited intrinsic mode function components ofbrightness information of at least two monitoring points in theplurality of monitoring points on the display image are extracted byusing the convolutional neural network. An average value of brightnessescorresponding to the band-limited intrinsic mode function components ofthe brightness information of the at least two monitoring points iscalculated as the average brightness value of the display image.

Alternatively, the display image is in a shape of a polygon.Band-limited intrinsic mode function components of brightnessinformation of monitoring points including, located at each corner ofthe display image, at least one corresponding monitoring point in theplurality of monitoring points, and band-limited intrinsic mode functioncomponents of brightness information of a monitoring point in theplurality of monitoring points located at a center of the display imageare extracted by using the convolutional neural network. An averagevalue of brightnesses corresponding to the extracted band-limitedintrinsic mode function components of the brightness information of themonitoring points including, located at each corner, the at least onecorresponding monitoring point, and the monitoring point located at thecenter, is calculated as the average brightness value of the displayimage.

In some embodiments, extracting and fusing the plurality of band-limitedintrinsic mode function components of the display image by using theconvolutional neural network to obtain the average brightness value andthe brightness uniformity of the display image, includes: extractingband-limited intrinsic mode function components of a monitoring pointwith least brightness information in the plurality of monitoring pointsand band-limited intrinsic mode function components of a monitoringpoint with most brightness information in the plurality of monitoringpoints; and calculating a ratio of a brightness corresponding to theband-limited intrinsic mode function components of the monitoring pointwith least brightness information to a brightness corresponding to theband-limited intrinsic mode function components of the monitoring pointwith most brightness information, so as to obtain the brightnessuniformity of the display image.

In another aspect, a display defect detection apparatus is provided. Thedisplay defect detection apparatus includes a feature extractor and aconvolutional neural network classifier.

The feature extractor is coupled to an image collector. The featureextractor is configured to: obtain at least one display image of atleast one display to be detected collected by the image collector; andextract a plurality of band-limited intrinsic mode function componentsfrom a display image in the at least one display image by using acomplex variational mode decomposition method.

The convolutional neural network classifier is coupled to the featureextractor. The convolutional neural network classifier is configured to:extract and fuse the plurality of band-limited intrinsic mode functioncomponents, so as to obtain an average brightness value and a brightnessuniformity of the display image; and determine whether a display to bedetected in the at least one display to be detected corresponding to thedisplay image is qualified according to a preset classification rule andthe average brightness value and the brightness uniformity of thedisplay image.

In some embodiments, the display defect detection apparatus furtherincludes a preprocessor. The preprocessor is coupled between the imagecollector and the feature extractor. The preprocessor is configured to:obtain the display image of the display to be detected collected by theimage collector; preprocess the display image; and transmit thepreprocessed display image to the feature extractor. The preprocessingincludes at least one of image cropping, graying and filtering.

In yet another aspect, a display defect detection system is provided.The display defect detection system includes an image collector, and thedisplay defect detection apparatus in any one of the above embodiments.The display defect detection apparatus is coupled to the imagecollector. The image collector is configured to collect at least onedisplay image of at least one display to be detected.

In yet another aspect, an electronic device is provided. The electronicdevice includes a memory and a processor. The memory stores computerprogram instructions. The processor is configured to run the computerprogram instructions to execute the display defect detection method inany one of the above embodiments.

In yet another aspect, a non-transitory computer-readable storage mediumis provided. The non-transitory computer-readable storage medium storescomputer program instructions. When the computer program instructionsrun on a processor, the processor executes the display defect detectionmethod in any one of the above embodiments.

In yet another aspect, a computer program product is provided. Thecomputer program product includes computer program instructions. Whenthe computer program instructions are executed on a computer, thecomputer program instructions cause the computer to execute the displaydefect detection method in any one of the above embodiments.

In yet another aspect, a computer program is provided. When the computerprogram is executed on a computer, the computer program causes thecomputer to execute the display defect detection method in any one ofthe above embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe technical solutions in the present disclosure moreclearly, accompanying drawings to be used in some embodiments of thepresent disclosure will be introduced briefly below. Obviously, theaccompanying drawings to be described below are merely accompanyingdrawings of some embodiments of the present disclosure, and a person ofordinary skill in the art may obtain other drawings according to thesedrawings. In addition, the accompanying drawings to be described belowmay be regarded as schematic diagrams, and are not limitations on anactual size of a product an actual process of a method, and an actualtiming of a signal involved in the embodiments of the presentdisclosure.

FIGS. 1 to 5 each are a flow diagram of a display defect detectionmethod, in accordance with some embodiments of the present disclosure;

FIG. 6 is a workflow diagram of a complex variational mode decompositionof a detection method of a display defect, in accordance with someembodiments of the present disclosure;

FIG. 7 is a structural diagram of a display defect detection apparatus,in accordance with some embodiments of the present disclosure;

FIG. 8 is a display image of a display to be detected, in accordancewith some embodiments of the present disclosure;

FIG. 9 is a structural diagram of a backlight module, in according withsome embodiments of the present disclosure;

FIG. 10 is a sectional view of the backlight module in FIG. 9 takenalong the P plane; and

FIG. 11 is a structural diagram of an electronic device, in accordancewith some embodiments of the present disclosure.

DETAILED DESCRIPTION

Technical solutions in some embodiments of the present disclosure willbe described clearly and completely below with reference to theaccompanying drawings. Obviously, the described embodiments are merelysome but not all embodiments of the present disclosure. All otherembodiments obtained by a person of ordinary skill in the art based onthe embodiments of the present disclosure shall be included in theprotection scope of the present disclosure.

Unless the context requires otherwise, throughout the description andthe claims, the term “comprise” and other forms thereof such as thethird-person singular form “comprises” and the present participle form“comprising” are construed as an open and inclusive meaning, i.e.,“including, but not limited to.” In the description of thespecification, the terms such as “one embodiment,” “some embodiments,”“exemplary embodiments,” “an example,” “specific example” or “someexamples” are intended to indicate that specific features, structures,materials or characteristics related to the embodiment(s) or example(s)are included in at least one embodiment or example of the presentdisclosure. Schematic representations of the above terms do notnecessarily refer to the same embodiment(s) or example(s). In addition,the specific features, structures, materials or characteristics may beincluded in any one or more embodiments or examples in any suitablemanner.

Hereinafter, the terms such as “first” and “second” are only used fordescriptive purposes, and are not to be construed as indicating orimplying relative importance or implicitly indicating the number ofindicated technical features. Thus, a feature defined with “first” or“second” may explicitly or implicitly include one or more of thefeatures. In the description of the embodiments of the presentdisclosure, the term “a plurality of/the plurality of” means two or moreunless otherwise specified.

In the description of some embodiments, the term “coupled” andextensions thereof are used. For example, the term “coupled” is used inthe description of some embodiments to indicate that two or morecomponents are in direct physical or electrical contact with each other.

The phrase “A and/or B” includes following three combinations: only A,only B, and a combination of A and B.

The use of the phase “configured to” herein means an open and inclusiveexpression, which does not exclude devices that are applicable to orconfigured to perform additional tasks or steps.

In addition, the use of the phase “based on” means openness andinclusiveness, since a process, step, calculation or other action thatis “based on” one or more stated conditions or values may, in practice,be based on additional conditions or values exceeding those stated.

As used herein, the term “substantially” includes a stated value and anaverage value within an acceptable range of deviation of a particularvalue. The acceptable range of deviation is determined by a person ofordinary skill in the art, considering measurement in question anderrors associated with measurement of a particular quantity (i.e.,limitations of a measurement system).

Exemplary embodiments are described herein with reference to sectionalviews and/or plan views as idealized exemplary drawings. In theaccompanying drawings, thicknesses of layers and sizes of regions areenlarged for clarity. Thus, variations in shape relative to theaccompanying drawings due to, for example, manufacturing techniquesand/or tolerances may be envisaged. Therefore, the exemplary embodimentsshould not be construed to be limited to the shapes of regions shownherein, but to include deviations in shape due to, for example,manufacturing. For example, an etched region shown in a rectangularshape generally has a curved feature. Therefore, the regions shown inthe accompanying drawings are schematic in nature, and their shapes arenot intended to show actual shapes of the regions in a device, and arenot intended to limit the scope of the exemplary embodiments.

In the related art, an artificial vision detection method is generallyused for the Hotspot phenomenon of the display picture of the display.However, this method is easily interfered by human subjective factorsand external environment, and lacks unified criteria for quantificationof the Hotspot phenomenon, so that an accuracy of detecting the Hotspotphenomenon cannot be ensured.

Based on this, some embodiments of the present disclosure provide adisplay defect detection method. As shown in FIG. 1 , the display defectdetection method includes following S10 to S13.

In S10, display image(s) of display(s) to be detected are collected.

It can be understood that the display image is an image displayed on adisplay screen of the display to be detected.

For example, display images of 95 to 105 displays to be detected may becollected as test images.

In S11, a plurality of band-limited intrinsic mode function (BLIMF)components are extracted from the display image by using a complexvariational mode decomposition (CVMD) method.

It will be noted that the complex variational mode decomposition methodrequires an establishment of an overall framework shown in FIG. 6 , soas to decompose signals of the display image by iterating out centerfrequencies and bandwidths of respective modal components. The complexvariational mode decomposition method shows a good robustness to noisesignals, and may be applied to decomposition of low signal-to-noiseratio signals and removal of noise signals in the signals of the displayimage.

In a process of decomposing the signals of the display image, for thecomplex variational mode decomposition method, the number of requiredmodal components may be artificially set, so that a redundancy of themodal components is reduced. Compared with an empirical modedecomposition (EMD) method and an improved algorithm thereof, thecomplex variational mode decomposition method solves problems ofendpoint effect and modal component mixing of the empirical modedecomposition method in view of the decomposition result.

In S12, the plurality of band-limited intrinsic mode function componentsof the display image are extracted and fused by using a convolutionalneural network (CNN), so as to obtain an average brightness value and abrightness uniformity of the display image.

It will be noted that the convolutional neural network is one of corealgorithms in the field of image recognition, and is a neural networkfor processing data with similar network structure. The convolutionalneural network includes an input layer, a hidden layer and an outputlayer.

Moreover, the “average brightness value” represents an overallbrightness of the display image, which may be a brightness value of anyregion of the display image or an average of brightness values of aplurality of regions of the display image.

The “brightness uniformity” represents an overall brightness uniformityof the display image, and the greater the brightness uniformity, thebetter the overall brightness uniformity of the display image.

The plurality of band-limited intrinsic mode function components of thedisplay image are extracted and fused by using the convolutional neuralnetwork, so that determination features of the display image, i.e., theaverage brightness value and the brightness uniformity of the displayimage, are obtained.

In S13, it is determined whether the display to be detectedcorresponding to the display image is qualified according to a presetclassification rule and the average brightness value and the brightnessuniformity of the display image.

It can be understood that the “preset classification rule” gives aclassification rule of the determination features of the display image,i.e., a classification rule for the average brightness value and thebrightness uniformity of the display image. The preset classificationrule will be described below.

According to the preset classification rule, if the average brightnessvalue and the brightness uniformity of the display image satisfy thepreset classification rule (which indicates that the Hotspot phenomenonof the display image is slight, and does not affect the display qualityof the display), the display to be detected corresponding to the displayimage is determined to be qualified; and if the average brightness valueand the brightness uniformity of the display image do not satisfy thepreset classification rule (which indicates that the Hotspot phenomenonof the display image is serious, and affects the display quality of thedisplay), the display to be detected corresponding to the display imageis determined to be unqualified.

In the above detection method, the plurality of band-limited intrinsicmode function components are extracted from the display image by usingthe complex variational mode decomposition method, so that the noisesignals in the signals of the display image may be removed.

The plurality of band-limited intrinsic mode function components areextracted and fused by using the convolutional neural network, so as toobtain the average brightness value and the brightness uniformity of thedisplay image for determining the display image. The average brightnessvalue and the brightness uniformity of the display image are determinedby using the unified preset classification rule, so as to realize thedetection of the Hotspot phenomenon of the display image, therebydetermining whether the display to be detected corresponding to thedisplay image is qualified through the detection of the Hotspotphenomenon of the display image.

In some embodiments, the preset classification rule includes as follows.If the average brightness value of the display image is greater than orequal to a preset brightness value, and the brightness uniformity of thedisplay image is greater than or equal to a preset brightnessuniformity, the display to be detected corresponding to the displayimage is determined to be qualified; if not, the display to be detectedcorresponding to the display image is determined to be unqualified.

It will be noted that the “preset brightness value” refers to abrightness value of the display image that satisfies a standard, and isused for determining whether the average brightness value of the displayimage satisfies the standard. If the average brightness value of thedisplay image is greater than or equal to the preset brightness value,the average brightness value of the display image is determined tosatisfy the standard; and if the average brightness value of the displayimage is less than the preset brightness value, the average brightnessvalue of the display image is determined not to satisfy the standard.

The “preset brightness uniformity” refers to a brightness uniformity ofthe display image that satisfies a standard, and is used for determiningwhether the brightness uniformity of the display image satisfies thestandard. If the brightness uniformity of the display image is greaterthan or equal to the preset brightness uniformity, the brightnessuniformity of the display image is determined to satisfy the standard;and if the brightness uniformity of the display image is less than thepreset brightness uniformity, the brightness uniformity of the displayimage is determined not to satisfy the standard.

In a case where the average brightness value of the display imagesatisfies the standard, and the brightness uniformity of the displayimage satisfies the standard, the display to be detected correspondingto the display image is determined to be qualified. In a case where theaverage brightness value of the display image does not satisfy thestandard, or the brightness uniformity of the display image does notsatisfy the standard, or the average brightness value and the brightnessuniformity of the display image do not satisfy respective standards, thedisplay to be detected corresponding to the display image is determinedto be unqualified.

In some embodiments, before the display image(s) of the display(s) to bedetected are collected in S10, as shown in FIG. 4 , the display defectdetection method further includes following S20 to S23.

In S20, display images of a plurality of sample displays are collectedas sample images, and reference results of whether the sample displayscorresponding to respective sample images are qualified are obtained.

It can be understood that the sample image is an image displayed on adisplay screen of the sample display.

For example, display images of 25 to 35 sample displays may be collectedas sample images.

The “reference result” may be a result of detecting whether the sampledisplay corresponding to the sample image is qualified by using theartificial vision detection method, or a result of knowing whether thesample display is qualified through a detection in advance.

In S21, it is determined whether a sample display that each sample imagecorresponds to is qualified by using the convolutional neural network,so as to obtain actual detection results of whether the plurality ofsample displays are qualified. The convolutional neural network includesdetection parameters.

For example, the detection parameters include a kernel functionparameter and a penalty parameter.

It will be noted that a detection accuracy of the convolutional neuralnetwork is related to the detection parameters, i.e., related to thekernel function parameter and the penalty parameter. The detectionaccuracy of the convolutional neural network may be adjusted byadjusting the detection parameters.

In S22, the actual detection result of the sample display is comparedwith a respective reference result to determine whether the actualdetection result is consistent with the respective reference result, andthe detection parameters of the convolutional neural network areadjusted according to a comparison result.

For example, if the actual detection result of the sample display isinconsistent with the respective reference result (which indicates thatthe detection accuracy of the convolutional neural network does notsatisfy a detection standard), the detection parameters of theconvolutional neural network need to be adjusted to improve thedetection accuracy of the convolutional neural network.

In S23, adjusting the detection parameters of the convolutional neuralnetwork repeatedly, until the actual detection results of the sampledisplays are stable.

It can be understood that the detection parameters of the convolutionalneural network are repeatedly adjusted, until the actual detectionresult of each sample display is determined to be consistent with arespective reference result by using the convolutional neural network.This indicates that the detection accuracy of the convolutional neuralnetwork satisfies the detection standard, and the convolutional neuralnetwork may be used for the display defect detection.

In some embodiments, before determining whether the sample display thateach sample image corresponds to is qualified by using the convolutionalneural network in S21, as shown in FIG. 4 , the display defect detectionmethod further includes following S201.

In S201, a plurality of band-limited intrinsic mode function componentsare extracted from the sample image by using the complex variationalmode decomposition method. Thus, noise signals in signals of the sampleimage may be removed.

In some embodiments, as shown in FIG. 5 , in S21, determining whetherthe sample display device that each sample image corresponds to isqualified by using the convolutional neural network to obtain the actualdetection results of whether the plurality of sample displays arequalified, includes following S210 and S211.

In S210, the plurality of band-limited intrinsic mode functioncomponents of the sample image are extracted and fused by using theconvolutional neural network, so as to obtain an average brightnessvalue and a brightness uniformity of the sample image.

The plurality of band-limited intrinsic mode function components of thesample image are extracted and fused by using the convolutional neuralnetwork, so that determination features of the sample image, i.e., theaverage brightness value and the brightness uniformity of the sampleimage, are obtained.

In S211, it is determined whether the sample display corresponding tothe sample image is qualified according to a preset classification ruleand the average brightness value and the brightness uniformity of thesample image.

According to the preset classification rule, if the average brightnessvalue and the brightness uniformity of the sample image satisfy thepreset classification rule, the sample display corresponding to thesample image is determined to be qualified; and if the averagebrightness value, the brightness uniformity, or the average brightnessvalue and the brightness uniformity of the sample image do not satisfythe preset classification rule, the sample display corresponding to thesample image is determined to be unqualified.

In some embodiments, as shown in FIG. 2 , after the display image(s) ofthe display(s) to be detected are collected in S10, the display defectdetection method further includes following S101.

In S101, the display image is preprocessed. The preprocessing includesat least one of image cropping, graying and filtering.

For example, one of a component method, a maximum method, an averagemethod or a weighted average method may be used in the grayingprocessing.

For example, the filtering processing may be one of mean filtering,median filtering, maximum-minimum filtering, bilateral filtering orguided filtering.

The image cropping is performed on the display image, so that a size ofthe display image meets requirements. The graying processing isperformed on the display image to prepare for a subsequent processing ofthe image. The filtering processing is performed on the display imageafter the graying processing, which is conducive to reducing the noisesignals in the signals of the display image.

In some embodiments, as shown in FIG. 2 , after the display image(s) ofthe display(s) to be detected are collected in S10, the display defectdetection method further includes S102.

In S102, as shown in FIG. 8 , a plurality of monitoring points P areprovided on the display image 1, and brightness information of theplurality of monitoring points P is obtained.

The plurality of monitoring points P are arranged in an array. Adistance a between two adjacent monitoring points P in a first directionX is substantially equal to a distance b between two adjacent monitoringpoints P in a second direction Y. The first direction X and the seconddirection Y intersect. For example, FIG. 8 shows the first direction Xand the second direction Y that are perpendicular to each other.

The plurality of monitoring points P are arranged in the array, so thatthe monitoring points P are uniformly arranged on the display image 1.Thus, by obtaining the brightness information of the monitoring pointsP, brightness information of the display image 1 is accurately obtained.

In some embodiments, as shown in FIG. 3 , in S11, extracting theplurality of band-limited intrinsic mode function components from thedisplay image by using the complex variational mode decompositionmethod, includes following S110 and S111.

In S110, brightness information of each monitoring point on the displayimage is decomposed into a plurality of modal components.

In S111, noise component(s) in the plurality of modal components of themonitoring point are removed to extract band-limited intrinsic modefunction components in the plurality of modal components of themonitoring point.

Through the above method, the noise component(s) of the brightnessinformation of each monitoring point on the display image are removed toextract the band-limited intrinsic mode function components of themonitoring point, which is conducive to improving an accuracy ofdetecting the average brightness value and the brightness uniformity ofthe display image in subsequent steps.

For example, as shown in FIG. 6 , the workflow of the complexvariational mode decomposition includes following S30 to S36.

In S30, a signal is converted to a complex domain by using thefunctional equation 1-1. It can be understood that the “signal” refersto a signal containing the brightness information of the monitoringpoint.

$\begin{matrix}{Z(f) = \left\lbrack {1 + {sgn}(f)} \right\rbrack X(f)} & \text{­­­(1-1)}\end{matrix}$

In the equation 1-1, Z(f) represents the signal converted to the complexdomain; sgn(f) is a step function; and X(f) represents the signalcontaining the brightness information of the monitoring point.

In S31, relevant information n is initialized by using the functionalequation 1-2.

$\begin{matrix}{n = 0,\left\{ {\hat{u}}_{k}^{1} \right\},\left\{ {\hat{\omega}}_{k}^{1} \right\},{\hat{\lambda}}^{1}} & \text{­­­(1-2)}\end{matrix}$

In the equation 1-2, k represents the number of the decomposed modalcomponents, and k is a positive integer;

{û_(k)¹}

represents a k-th decomposed modal component;

{ω̂_(k)¹}

represents a center frequency of the k-th decomposed modal component;and

λ̂¹

represents a bandwidth of each decomposed modal component. The number kof the decomposed modal components may be artificially set as needed, soas to reduce the redundancy of the modal components.

In S32, n is cyclically updated by using the functional equation 1-3.

$\begin{matrix}{n = n + 1} & \text{­­­(1-3)}\end{matrix}$

In S33, u_(k) is updated by using the functional equation 1-4, wherek=1:k.

$\begin{matrix}{{\hat{u}}_{k}^{n + 1} = \underset{\hat{u}k}{\arg\min}L\left( {\left\{ {\hat{u}}_{j < k}^{n + 1} \right\},\left\{ {\hat{u}}_{l \geq k}^{n} \right\},\left\{ {\hat{\omega}}_{l}^{n} \right\},{\hat{\lambda}}^{n}} \right)} & \text{­­­(1-4)}\end{matrix}$

The equation 1-4 represents a value of

û_(k)^(n + 1)

when

L({û_(j < k)^(n + 1)}, {û_(l ≥ k)^(n)}, {ω̂_(l)^(n)}, λ̂^(n))

is a minimum value.

In S34, ω_(k) is updated by using the functional equation 1-5, wherek=1:k.

$\begin{matrix}{{\hat{\omega}}_{k}^{n + 1} = \underset{\hat{\omega}\delta}{\arg\min}L\left( {\left\{ {\hat{u}}_{l}^{n + 1} \right\},\left\{ {\hat{u}}_{j < k}^{n} \right\},\left\{ {\hat{\omega}}_{i \geq k}^{n} \right\},{\hat{\lambda}}^{n}} \right)} & \text{­­­(1-5)}\end{matrix}$

The equation 1-5 represents a value of

ω̂_(k)^(n + 1)

when is a minimum value.

In S35, λ is updated by using the functional equation 1-6.

$\begin{matrix}{{\hat{\lambda}}^{n + 1} = {\hat{\lambda}}^{n} + \tau\left( {x - {\sum_{k}{\hat{u}}_{k}^{n + 1}}} \right)} & \text{­­­(1-6)}\end{matrix}$

In the equation 1-6,

τ(x − ∑_(k)û_(k)^(n + 1))

represents a noise tolerance.

In S36, determination is made according to the functional equation 1-7.

$\begin{matrix}{{\sum_{k}{\left\| {{\hat{u}}_{k}^{n + 1} - {\hat{u}}_{k}^{n}} \right\|_{2}^{2}l\left\| {\hat{u}}_{k}^{n} \right\|_{2}^{2}}}\left\langle \varepsilon \right.} & \text{­­­(1-7)}\end{matrix}$

In the equation 1-7, ε represents a precision convergence criterion.

If

∑_(k)∥û_(k)^(n + 1) − û_(k)^(n)∥₂²l∥û_(k)^(n)∥₂²⟨ε

holds, the band-limited intrinsic mode function component {u_(k)} isoutput. If

∑_(k)∥û_(k)^(n + 1) − û_(k)^(n)∥₂²l∥û_(k)^(n)∥₂²⟨ε

does not hold (which indicates that the decomposed modal component is anoise component), the workflow is returned to S32, and S32 to S36 arerepeated.

It can be understood that until k band-limited intrinsic mode functioncomponents are determined to be obtained, the workflow of the complexvariational mode decomposition ends.

In some embodiments, in S12, extracting and fusing the plurality ofband-limited intrinsic mode function components of the display image byusing the convolutional neural network to obtain the average brightnessvalue and brightness uniformity of the display image, includes followingstep(s).

Band-limited intrinsic mode function components of brightnessinformation of a monitoring point on the display image are extracted byusing the convolutional neural network, so that a brightnesscorresponding to the band-limited intrinsic mode function components ofthe monitoring point is used as the average brightness value of thedisplay image.

It will be noted that the single monitoring point that is extracted fromthe display image should be a representative monitoring point that mayrepresent the overall brightness of the display image.

For example, as shown in FIG. 8 , band-limited intrinsic mode functioncomponents of brightness information of a monitoring point P1 located ata center of the display image 1 may be extracted, so that a brightnesscorresponding to the band-limited intrinsic mode function components ofthe monitoring point P1 is used as the average brightness value of thedisplay image 1.

Alternatively, band-limited intrinsic mode function components ofbrightness information of monitoring points on the display image areextracted by using the convolutional neural network; and an averagevalue of brightnesses corresponding to the band-limited intrinsic modefunction components of the brightness information of the monitoringpoints is calculated as the average brightness value of the displayimage.

It can be understood that the average value of the brightnessescorresponding to the band-limited intrinsic mode function components ofthe brightness information of the monitoring points is used as theaverage brightness value of the display image to represent the overallbrightness of the display image, so that the overall brightness of thedisplay image may be accurately reflected.

For example, as shown in FIG. 8 , band-limited intrinsic mode functioncomponents of brightness information of all the monitoring points P onthe display image 1 may be extracted. An average value of brightnessescorresponding to the band-limited intrinsic mode function components ofthe brightness information of all the monitoring points P is calculatedas the average brightness value of display image 1.

Alternatively, as shown in FIG. 8 , in a case where the display image 1is in a shape of a polygon (e.g., rectangle), band-limited intrinsicmode function components of brightness information of monitoring points(e.g., monitoring points P2, P3, P4, and P5 shown in FIG. 8 ) including,located at each corner of the display image 1, at least onecorresponding monitoring point, and the band-limited intrinsic modefunction components of the brightness information of the monitoringpoint P1 located at the center of the display image are extracted byusing the convolutional neural network. An average value of brightnessescorresponding to the extracted band-limited intrinsic mode functioncomponents of the brightness information of the monitoring points (e.g.,the monitoring points P2, P3, P4 and P5) including, located at eachcorner, the at least one corresponding monitoring point, and themonitoring point P1 located at the center, is calculated as the averagebrightness value of the display image 1.

The average value of the brightnesses corresponding to the band-limitedintrinsic mode function components of the brightness information of themonitoring points including, located at each corner of the displayimage, the at least one corresponding monitoring point, and themonitoring point located at the center of the display image, iscalculated as the average brightness value of the display image. Thesemonitoring points are distributed in various regions of the displayimage. Thus, the overall brightness of the display image may bereflected more accurately.

In some embodiments, in S12, extracting and fusing the plurality ofband-limited intrinsic mode function components of the display image byusing the convolutional neural network to obtain the average brightnessvalue and brightness uniformity of the display image, further includesfollowing steps.

Band-limited intrinsic mode function components of a monitoring pointwith least brightness information and band-limited intrinsic modefunction components of a monitoring point with most brightnessinformation are extracted; and a ratio of a brightness corresponding tothe band-limited intrinsic mode function components of the monitoringpoint with least brightness information to a brightness corresponding tothe band-limited intrinsic mode function components of the monitoringpoint with most brightness information is calculated, so as to obtainthe brightness uniformity of the display image.

It can be understood that the ratio of the brightness corresponding tothe band-limited intrinsic mode function components of the monitoringpoint with least brightness information to the brightness correspondingto the band-limited intrinsic mode function components of the monitoringpoint with most brightness information is used as the brightnessuniformity of the display image to represent the overall brightnessuniformity of the display image.

Some embodiments of the present disclosure further provide a displaydefect detection apparatus. As shown in FIG. 7 , the display defectdetection apparatus 100 includes a feature extractor 101 and aconvolutional neural network classifier 102.

The feature extractor 101 is coupled to an image collector 201. Thefeature extractor 101 is configured to: obtain display image(s) ofdisplay(s) to be detected collected by the image collector 201; andextract a plurality of band-limited intrinsic mode function componentsfrom the display image by using a complex variational mode decompositionmethod.

The convolutional neural network classifier 102 is coupled to thefeature extractor 101. The convolutional neural network classifier 102is configured to: extract and fuse the plurality of band-limitedintrinsic mode function components, so as to obtain an averagebrightness value and a brightness uniformity of the display image; anddetermine whether the display to be detected corresponding to thedisplay image is qualified according to a preset classification rule andthe average brightness value and the brightness uniformity of thedisplay image.

In the display defect detection apparatus 100, the feature extractor 101extracts the plurality of band-limited intrinsic mode functioncomponents from the display image by using the complex variational modedecomposition method, so that noise signals in signals of the displayimage may be removed. The convolutional neural network classifier 102extracts and fuses the plurality of band-limited intrinsic mode functioncomponents, so as to obtain the average brightness value and thebrightness uniformity of the display image for determining the displayimage. The average brightness value and the brightness uniformity of thedisplay image are determined by using the unified preset classificationrule, so as to realize the detection of the Hotspot phenomenon of thedisplay image, thereby determining whether the display to be detectedcorresponding to the display image is qualified through the detection ofthe Hotspot phenomenon of the display image.

In some embodiments, as shown in FIG. 7 , the display defect detectionapparatus 100 further includes a preprocessor 103. The preprocessor 103is coupled between the image collector 201 and the feature extractor101. The preprocessor 103 is configured to: obtain the display image ofthe display to be detected collected by the image collector 201;preprocess the display image; and transmit the preprocessed displayimage to the feature extractor 101. The preprocessing includes at leastone of image cropping, graying and filtering.

In the display defect detection apparatus 100, the preprocessor 103performs the image cropping on the display image to make a size of thedisplay image meet requirements, performs the graying processing on thedisplay image to prepare for a subsequent processing of the image, andperforms the filtering processing on the display image after the grayingprocessing to reduce the noise signals in the signals of the displayimage.

Some embodiments of the present disclosure further provide a displaydefect detection system. As shown in FIG. 7 , the display defectdetection system 200 includes the image collector 201 and the displaydefect detection apparatus 100 in any one of the above embodiments. Thedisplay defect detection apparatus 100 is coupled to the image collector201. The image collector 201 is configured to collect the displayimage(s) of the display(s) to be detected.

The display defect detection system 200 has the same beneficial effectsas the display defect detection apparatus 100 in any one of the aboveembodiments, which will not be repeated here.

As shown in FIGS. 9 and 10 , some embodiments of the present disclosurefurther provide a backlight module. The backlight module 300 includes aback plate 301, and a reflective sheet 302, a light guide plate (LGP)303 and a plurality of optical films 304 that are stacked on the backplate 301 in sequence.

As shown in FIGS. 9 and 10 , a plurality of support legs 3011 areprovided on a surface (i.e., a bottom surface) of the back plate 301away from a light exit side M of the backlight module 300, and are usedfor supporting the backlight module 300. Rubber pads 312 are installedat four corners of a surface of the back plate 301 proximate to thelight exit side M of the backlight module 300. The rubber pads 312 areused for supporting the plurality of optical films 304 in a directionperpendicular to a plane where the back plate 301 is located, and forproviding buffer protection for the plurality of optical films 304 alongthe plane where the back plate 301 is located. A compression ratio ofthe rubber pad 312 is 60%. An insulating tape 305 is adhered to the backplate 301, and may be used for preventing static electricity fromentering the backlight module 300. A black light-shielding tape 306 isfurther adhered to the back plate 301, and may prevent light in thebacklight module 300 from leaking out through holes in the back plate301.

As shown in FIG. 9 , the reflective sheet 302 may reflect lightreflected to a surface thereof to the light guide plate 303, so as toimprove a utilization rate of a light source. Moreover, the reflectivesheet 302 includes a bending portion 3021 located at an edge of thereflective sheet 302, and the bending portion 3021 is bent toward adirection away from the back plate 301, so that a side of the reflectivesheet 302 may also reflect light, which may further improve theutilization rate of the light source.

As shown in FIG. 9 , the backlight module 300 further includes aplurality of light-emitting devices 307 fixed to a surface of the lightguide plate 303. The light guide plate 303 propagates light emitted fromthe plurality of light-emitting devices 307 through a light exit surfaceof the light guide plate 303.

For example, the light-emitting device 307 is a light-emitting diode(LED). An orthographic projection of the light-emitting diode on thesurface of the light guide plate 303 is in a shape of a rectangle, and alength of the rectangle is in a range of 28 mm to 32 mm, inclusive. Forexample, the length of the rectangle is 28 mm, 29 mm, 30 mm, 31 mm or 32mm. A width of the rectangle is in a range of 12 mm to 16 mm, inclusive.For example, the width of the rectangle is 12 mm, 13 mm, 14 mm, 15 mm or16 mm.

For example, the light-emitting devices 307 are adhered to the surfaceof the light guide plate 303 through an adhesive tape 308, and aflexible printed circuit (FPC) 309 is further adhered to the adhesivetape 308. The flexible printed circuit 309 is electrically connected tothe plurality of light-emitting devices 307, so as to provide voltagesignals to the plurality of light-emitting devices 307.

As shown in FIGS. 9 and 10 , a plurality of protrusions are provided inan edge region, close to the plurality of light-emitting devices 307, ofa surface of the light guide plate 303 away from the back plate 301.Accordingly, openings are provided in edges of the plurality of opticalfilms 304. An opening of the optical film 304 corresponds to aprotrusion on the light guide plate 303, so that the opening of theoptical film 304 may be engaged with the protrusion of the light guideplate 303, thereby limiting a displacement of the optical film 304 alonga length extending direction of the edge of the optical film 304, so asto prevent the displacement of the optical film 304 from affecting aneffect of the optical film 304 on exit light.

Moreover, each optical film 304 is adhered to the light guide plate 303through the adhesive tape 308, so that the optical film 304 is preventedfrom moving relative to the light guide plate 303.

For example, as shown in FIG. 9 , the plurality of optical films 304include a first diffusion sheet 3041, a first prism sheet 3042, a secondprism sheet 3043 and a second diffusion sheet 3044 that are stacked onthe light guide plate 303 in sequence.

The first diffusion sheet 3041 atomizes light through refraction andreflection of diffusion substances included therein, so as to increasean amount of exit light along a direction perpendicular to the firstdiffusion sheet 3041 to improve front luminance, thereby concentratinglight emitted from the light guide plate 303 to be uniformly projectedonto the first prism sheet 3042. The first prism sheet 3042 is a 90°lower prism sheet (i.e., a propagation direction of light is deflectedby 90° after passing through the lower prism sheet). The second prismsheet 3043 is a 0° upper prism sheet (i.e., the propagation direction oflight is deflected by 0° after passing through the upper prism sheet).When light passes through the first prism sheet 3042 and the secondprism sheet 3043, only light incident within a preset angle range mayexit through refraction, and light outside the preset angle range isreflected because a refraction condition is not satisfied. The reflectedlight propagates to the reflective sheet 302, and propagates to thefirst prism sheet 3042 again after the reflection of the reflectivesheet 302. In this way, most of the light may exit from the first prismsheet 3042 and the second prism sheet 3043 after a plurality ofreflections, so that the utilization rate of the light source may beimproved. The second diffusion sheet 3044 may atomize light emitted fromthe second prism sheet 3043 to make the light exit uniformly, and mayprotect the second prism sheet 3043.

For example, as shown in FIG. 9 , a first protrusion 3031, a secondprotrusion 3032 and a third protrusion 3033 are provided on the surfaceof the light guide plate 303 away from the back plate 301. The secondprotrusion 3032 is located on a surface of the first protrusion 3031away from the light guide plate 303, and the second protrusion 3032 andthe first protrusion 3031 are of an integral structure. The thirdprotrusion 3033 is located on a surface of the second protrusion 3032away from the light guide plate 303, and the third protrusion 3033 andthe second protrusion 3032 are of an integral structure. That is, thefirst protrusion 3031, the second protrusion 3032 and the thirdprotrusion 3033 are of an integral structure.

Accordingly, the first diffusion sheet 3041 is provided with a firstopening H1 therein, the first prism sheet 3042 is provided with a secondopening H2 therein, and the second diffusion sheet 3044 is provided witha third opening H3 therein. The first opening H1 of the first diffusionsheet 3041 is engaged with the first protrusion 3031 of the light guideplate 303, the second opening H2 of the first prism sheet 3042 isengaged with the second protrusion 3032 of the light guide plate 303,and the third opening H3 of the second diffusion sheet 3044 is engagedwith the third protrusion 3033 of the light guide plate 303.

Referring to FIG. 10 , along a direction E parallel to a plane where thelight guide plate 303 is located and perpendicular to the surface of thelight guide plate 303 where the plurality of light-emitting devices 307are disposed, the second opening H2 of the first prism sheet 3042 andthe second protrusion 3032 of the light guide plate 303 have a distanceS therebetween.

In the related art, along a direction parallel to a plane where a lightguide plate is located and perpendicular to a surface of the light guideplate where a plurality of light-emitting devices are disposed, a secondopening of a first prism sheet and a second protrusion of the lightguide plate are in contact, and have no distance therebetween. In thisway, light may propagate from the second protrusion of the light guideplate to the first prism sheet, so that more light exits from a lightexit region of the backlight module corresponding to the secondprotrusion. That is, more light exits from the light exit region of thebacklight module close to the light-emitting devices. Thus, a region ofa display picture of a display close to the light-emitting devices has alarge brightness, and a region of the display picture of the displayaway from the light-emitting devices has a small brightness, so that thedisplay picture shows the Hotspot phenomenon.

In some embodiments of the present disclosure, the Hotspot phenomenon ofthe display image of the display is detected by using the display defectdetection method. In a case where the display to be detectedcorresponding to the display image is determined to be unqualified, thedistance S between the second opening H2 and the second protrusion 3032may be set in the direction E, so that light cannot directly propagatefrom the second protrusion of the light guide plate to the first prismsheet. The light exiting from the second protrusion of the light guideplate propagates to the first prism sheet through air, so that apropagation efficiency of the light incident on the first prism sheetthrough the second opening H2 may be reduced. Thus, an amount of lightexiting from a light exit region of the backlight module close to thelight-emitting devices is reduced, so that a brightness of a region ofthe display picture close to the light-emitting devices is reduced, soas to reduce the Hotspot phenomenon of the display picture of thedisplay.

Then, the Hotspot phenomenon of the display image of the display isdetected again by using the display defect detection method. In a casewhere the display to be detected corresponding to the display image isdetermined to be still unqualified, the Hotspot phenomenon of thedisplay picture of the display may be reduced by increasing the distanceS between the second opening H2 and the second protrusion 3032, untilthe display to be detected corresponding to the display image isdetermined to be qualified. The final distance S is obtained.

As shown in FIG. 9 , the backlight module 300 further includes a rubberframe 310 around the backlight module 300, and is used for supportingthe display. A foam double-sided adhesive 311 is provided on a surfaceof the rubber frame 310 proximate to the light exit side M of thebacklight module 300, and is used for adhering the display to thebacklight module 300, so as to prevent the display screen from beingdamaged caused by shaking of the display during transportation.

Some embodiments of the present disclosure further provide an electronicdevice. As shown in FIG. 11 , the electronic device 400 includes amemory 401 and a processor 402. The memory 401 stores computer programinstructions. The processor 402 is configured to run the computerprogram instructions to execute the display defect detection method inany one of the above embodiments.

Some embodiments of the present disclosure further provide anon-transitory computer-readable storage medium storing the computerprogram instructions. When the computer program instructions run on theprocessor, the processor executes the display defect detection method inany one of the above embodiments.

For example, the non-transitory computer-readable storage medium mayinclude, but is not limited to, a magnetic storage device (e.g., a harddisk, a floppy disk or a magnetic tape), an optical disk (e.g., acompact disk (CD), a digital versatile disk (DVD)), a smart card or aflash memory device (e.g., an erasable programmable read-only memory(EPROM), a card, a stick or a key driver). The various kinds ofnon-transitory computer-readable storage media described in the presentdisclosure may represent one or more devices and/or othermachine-readable storage media for storing information. The term“machine-readable storage media” may include, but is not limited to,wireless channels and various kinds of other media capable of storing,containing and/or carrying instructions and/or data.

Some embodiments of the present disclosure further provide a computerprogram product. The computer program product includes computer programinstructions. When the computer program instructions are executed on acomputer, the computer program instructions cause the computer toexecute the display defect detection method in any one of the aboveembodiments.

Some embodiments of the present disclosure further provide a computerprogram. When the computer program is executed on a computer, thecomputer program causes the computer to execute the display defectdetection method in any one of the above embodiments.

Beneficial effects of the non-transitory computer-readable storagemedium, the computer program product, and the computer program are thesame as the beneficial effects of the display defect detection method insome embodiments described above, and will not be repeated here.

The foregoing descriptions are merely specific implementations of thepresent disclosure. However, the protection scope of the presentdisclosure is not limited thereto. Changes or replacements that anyperson skilled in the art could conceive of within the technical scopeof the present disclosure shall be included in the protection scope ofthe present disclosure. Therefore, the protection scope of the presentdisclosure shall be subject to the protection scope of the claims.

What is claimed is:
 1. A display defect detection method, comprising:collecting at least one display image of at least one display to bedetected; extracting a plurality of band-limited intrinsic mode functioncomponents from a display image in the at least one display image byusing a complex variational mode decomposition method; extracting andfusing the plurality of band-limited intrinsic mode function componentsof the display image by using a convolutional neural network, so as toobtain an average brightness value and a brightness uniformity of thedisplay image; and determining whether a display to be detected in theat least one display to be detected corresponding to the display imageis qualified according to a preset classification rule and the averagebrightness value and the brightness uniformity of the display image. 2.The display defect detection method according to claim 1, wherein thepreset classification rule includes: if the average brightness value ofthe display image is greater than or equal to a preset brightness value,and the brightness uniformity of the display image is greater than orequal to a preset brightness uniformity, determining that the display tobe detected corresponding to the display image is qualified; and if not,determining that the display to be detected corresponding to the displayimage is unqualified.
 3. The display defect detection method accordingto claim 1, wherein before collecting the at least one display image ofthe at least one display to be detected, the display defect detectionmethod further comprises: collecting display images of a plurality ofsample displays as sample images; obtaining reference results of whetherthe sample displays corresponding to respective sample images arequalified; determining whether a sample display in the plurality ofsample displays that each sample image corresponds to is qualified byusing the convolutional neural network, so as to obtain actual detectionresults of whether the plurality of sample displays are qualified; theconvolutional neural network including detection parameters; comparingan actual detection result of a sample display in the plurality ofsample displays with a respective reference result to determine whetherthe actual detection result is consistent with the respective referenceresult; adjusting the detection parameters of the convolutional neuralnetwork according to a comparison result; and adjusting the detectionparameters of the convolutional neural network repeatedly, until theactual detection results of the plurality of sample displays are stable.4. The display defect detection method according to claim 3, whereinbefore determining whether the sample display that each sample imagecorresponds to is qualified by using the convolutional neural network,the display defect detection method further comprises: extracting aplurality of band-limited intrinsic mode function components from thesample image by using the complex variational mode decomposition method.5. The display defect detection method according to claim 4, whereindetermining whether the sample display that each sample imagecorresponds to is qualified by using the convolutional neural network,includes: extracting and fusing the plurality of band-limited intrinsicmode function components of the sample image by using the convolutionalneural network, so as to obtain an average brightness value and abrightness uniformity of the sample image; and determining whether thesample display corresponding to the sample image is qualified accordingto the preset classification rule and the average brightness value andthe brightness uniformity of the sample image.
 6. The display defectdetection method according to claim 3, wherein comparing the actualdetection result of the sample display with the respective referenceresult to determine whether the actual detection result is consistentwith the respective reference result, and adjusting the detectionparameters of the convolutional neural network according to thecomparison result, include: if the actual detection result of the sampledisplay is inconsistent with the respective reference result, adjustingthe detection parameters of the convolutional neural network.
 7. Thedisplay defect detection method according to claim 3, wherein thedetection parameters include a kernel function parameter and a penaltyparameter.
 8. The display defect detection method according to claim 1,wherein after collecting the at least one display image of the at leastone display to be detected, the display defect detection method furthercomprises: preprocessing the display image; the preprocessing includingat least one of image cropping, graying and filtering.
 9. The displaydefect detection method according to claim 1, wherein after collectingthe at least one display image of the at least one display to bedetected, the display defect detection method further comprises:disposing a plurality of monitoring points on the display image; andobtaining brightness information of the plurality of monitoring points;wherein the plurality of monitoring points are arranged in an array; adistance between two adjacent monitoring points in a first direction issubstantially equal to a distance between two adjacent monitoring pointsin a second direction; and the first direction and the second directionintersect.
 10. The display defect detection method according to claim 9,wherein extracting the plurality of band-limited intrinsic mode functioncomponents from the display image by using the complex variational modedecomposition method, includes: decomposing brightness information ofeach monitoring point on the display image into a plurality of modalcomponents by using the complex variational mode decomposition method;and removing at least one noise component in the plurality of modalcomponents of the monitoring point to extract band-limited intrinsicmode function components in the plurality of modal components of themonitoring point.
 11. The display defect detection method according toclaim 9, wherein extracting and fusing the plurality of band-limitedintrinsic mode function components of the display image by using theconvolutional neural network to obtain the average brightness value andthe brightness uniformity of the display image, includes: extractingband-limited intrinsic mode function components of brightnessinformation of a monitoring point in the plurality of monitoring pointson the display image by using the convolutional neural network, so thata brightness corresponding to the band-limited intrinsic mode functioncomponents of the monitoring point is used as the average brightnessvalue of the display image.
 12. The display defect detection methodaccording to claim 9, wherein extracting and fusing the plurality ofband-limited intrinsic mode function components of the display image byusing the convolutional neural network to obtain the average brightnessvalue and the brightness uniformity of the display image, includes:extracting band-limited intrinsic mode function components of amonitoring point with least brightness information in the plurality ofmonitoring points and band-limited intrinsic mode function components ofa monitoring point with most brightness information in the plurality ofmonitoring points; and calculating a ratio of a brightness correspondingto the band-limited intrinsic mode function components of the monitoringpoint with least brightness information to a brightness corresponding tothe band-limited intrinsic mode function components of the monitoringpoint with most brightness information, so as to obtain the brightnessuniformity of the display image.
 13. A display defect detectionapparatus, comprising: a feature extractor coupled to an imagecollector; wherein the feature extractor is configured to: obtain atleast one display image of at least one display to be detected collectedby the image collector; and extract a plurality of band-limitedintrinsic mode function components from a display image in the at leastone display image by using a complex variational mode decompositionmethod; and a convolutional neural network classifier coupled to thefeature extractor; wherein the convolutional neural network classifieris configured to: extract and fuse the plurality of band-limitedintrinsic mode function components, so as to obtain an averagebrightness value and a brightness uniformity of the display image; anddetermine whether a display to be detected in the at least one displayto be detected corresponding to the display image is qualified accordingto a preset classification rule and the average brightness value and thebrightness uniformity of the display image.
 14. The display defectdetection apparatus according to claim 13, further comprising: apreprocessor coupled between the image collector and the featureextractor; wherein the preprocessor is configured to: obtain the displayimage of the display to be detected collected by the image collector;preprocess the display image; and transmit the preprocessed displayimage to the feature extractor; wherein the preprocessing includes atleast one of image cropping, graying and filtering.
 15. A display defectdetection system, comprising: an image collector configured to collectat least one display image of at least one display to be detected; andthe display defect detection apparatus according to claim 13, thedisplay defect detection apparatus being coupled to the image collector.16. An electronic device comprising a memory and a processor, whereinthe memory stores computer program instructions; and the processor isconfigured to run the computer program instructions to execute thedisplay defect detection method according to claim
 1. 17. Anon-transitory computer-readable storage medium storing computer programinstructions, wherein when the computer program instructions run on aprocessor, the processor executes the display defect detection methodaccording to claim
 1. 18. The display defect detection method accordingto claim 9, wherein extracting and fusing the plurality of band-limitedintrinsic mode function components of the display image by using theconvolutional neural network to obtain the average brightness value andthe brightness uniformity of the display image, includes: extractingband-limited intrinsic mode function components of brightnessinformation of at least two monitoring points in the plurality ofmonitoring points on the display image by using the convolutional neuralnetwork; and calculating an average value of brightnesses correspondingto the band-limited intrinsic mode function components of the brightnessinformation of the at least two monitoring points as the averagebrightness value of the display image.
 19. The display defect detectionmethod according to claim 9, wherein the display image is in a shape ofa polygon; extracting and fusing the plurality of band-limited intrinsicmode function components of the display image by using the convolutionalneural network to obtain the average brightness value and the brightnessuniformity of the display image, includes: extracting band-limitedintrinsic mode function components of brightness information ofmonitoring points including, located at each corner of the displayimage, at least one corresponding monitoring point in the plurality ofmonitoring points, and band-limited intrinsic mode function componentsof brightness information of a monitoring point in the plurality ofmonitoring points located at a center of the display image by using theconvolutional neural network; and calculating an average value ofbrightnesses corresponding to the extracted band-limited intrinsic modefunction components of the brightness information of the monitoringpoints including, located at each corner, the at least one correspondingmonitoring point, and the monitoring point located at the center as theaverage brightness value of the display image.
 20. The display defectdetection method according to claim 2, wherein before collecting the atleast one display image of the at least one display to be detected, thedisplay defect detection method further comprises: collecting displayimages of a plurality of sample displays as sample images; obtainingreference results of whether the sample displays corresponding torespective sample images are qualified; determining whether a sampledisplay in the plurality of sample displays that each sample imagecorresponds to is qualified by using the convolutional neural network,so as to obtain actual detection results of whether the plurality ofsample displays are qualified; the convolutional neural networkincluding detection parameters; comparing an actual detection result ofa sample display in the plurality of sample displays with a respectivereference result to determine whether the actual detection result isconsistent with the respective reference result; adjusting the detectionparameters of the convolutional neural network according to a comparisonresult; and adjusting the detection parameters of the convolutionalneural network repeatedly, until the actual detection results of theplurality of sample displays are stable.